harujoh / Kelpnet
Licence: apache-2.0
Pure C# machine learning framework
Stars: ✭ 136
Programming Languages
csharp
926 projects
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KelpNet : Pure C# machine learning framework
/* SampleCode */
FunctionStack<float> nn = new FunctionStack<float>(
new Convolution2D<float>(1, 32, 5, pad: 2, name: "l1 Conv2D"),
new ReLU<float>(name: "l1 ReLU"),
new MaxPooling<float>(2, 2, name: "l1 MaxPooling"),
new Convolution2D<float>(32, 64, 5, pad: 2, name: "l2 Conv2D"),
new ReLU<float>(name: "l2 ReLU"),
new MaxPooling<float>(2, 2, name: "l2 MaxPooling"),
new Linear<float>(7 * 7 * 64, 1024, name: "l3 Linear"),
new ReLU<float>(name: "l3 ReLU"),
new Dropout<float>(name: "l3 DropOut"),
new Linear<float>(1024, 10, name: "l4 Linear")
);
- Samples: ・XOR ・CNN ・AlexNet ・VGG ・ResNet ・Others
- SampleData: ・MNIST ・FashionMNIST ・CIFAR 10/100
- Importable: ・CaffeModel ・ChainerModel ・ONNXModel
特徴
- 行列演算にライブラリを使用していないため、全てのアルゴリズムが可読になっています
- PyTorch や TensorFlow と同じ Define by Run を採用しています
- 並列演算にOpenCLを採用しているため、GPUだけでなくCPUやFPGA等の様々な演算装置で処理を並列化できます
※OpenCLを使用するためには対応するドライバの追加インストールが必要になることがあります
- Intel製 CPU or GPU: https://software.intel.com/en-us/articles/opencl-drivers
- AMD製 CPU or GPU: http://www.amd.com/ja-jp/solutions/professional/hpc/opencl
- Nvidia製 GPU: https://developer.nvidia.com/opencl
C#で作られているメリット
- 開発環境の構築がカンタンで、プログラミングの初学者にも学びやすい言語です
- .Net標準のFormやUnity等、処理結果を視覚的に表現するための選択肢が豊富です
- PCやモバイル端末、組み込み機器等、様々なプラットフォームに向けた開発ができます
連絡方法
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現在の開発状況なども Twitter でご確認いただけます
Twitter: https://twitter.com/harujoh
動作環境
Libraries: .NET Standard 2.0 or 2.1
Samples: .NET Framework 4.6.1
実装済み関数
- Connections: ・Convolution2D ・Deconvolution2D ・EmbedID ・Linear ・LSTM
- Activations: ・ELU ・LeakyReLU ・ReLU ・ReLU6 ・Sigmoid ・Tanh ・Softmax ・Softplus ・Swish ・Mish
- Poolings: ・AveragePooling2D ・MaxPooling2D
- Normalize: ・BatchNormalization ・LRN
- Noise: ・Dropout ・StochasticDepth
- LossFunctions: ・MeanSquaredError ・SoftmaxCrossEntropy
- Optimizers: ・AdaBound ・AdaDelta ・AdaGrad ・Adam ・AdamW ・AMSBound ・AMSGrad ・MomentumSGD ・RMSprop ・SGD
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